index.Rmd

---
title: "Brisbane Flooding Vulnerability Index"
author:
- Catherine Kim
- Kaitlyn Brown
- Richard Cottrell
- Jess Hopf
- Aiden Price
- Connie Susilawati
date: "29/06/2022"
output:
  ioslides_presentation:
    css: assets/torino_style.css
  beamer_presentation: default
subtitle: ''
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, cache=TRUE, message=FALSE, results=FALSE, warning=FALSE, tidy.opts=list(width.cutoff=38, indent=2), tidy=TRUE)
library(sf)
library(tidyverse)
library(ozmaps)
qut_blue = "#00407a"
qut_dark_blue = "#06ABE8"
```

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<style type="text/css">
slides > slide:not(.nobackground):after {
  content: '';
}
</style>

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<style>
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  margin-top: -85px;
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# Brisbane Flooding Hackathon Presentation | For R Ladies Brisbane

## {.fullslide}

<center>
<img src="images/UQ_flooding.jpeg">
</center>

<div style="position: absolute; left: 50px; top:50px;background-color:#00407a72";>
<p><font color="white">
<br><strong> Motivation </strong><br>
<br> Impacts beyond flood inundation <br>
<p style = "margin-left: 40px"> Access to services - shops, hospitals, petrol stations </p>
<p style = "margin-left: 40px"> Loss of power </p>
<p style = "margin-left: 40px"> Road and public transport closures </p>
</font></p></div>

## {.fullslide}

<center>
<img src="images/flood_map.png">
</center>

<div style="position: absolute; left: 300px; top:150px;background-color:#00407a72";>
<p><font color="white">
<br><strong> What is the gap? </strong><br>
</font></p></div>

## What is vulnerability?

**Vulnerability** = ***Exposure*** + ***Sensitivity*** + ***Adaptive Capacity*** <br><font size="1"> IPCC Climate Change 2001: Impacts, Adaptation, and Vulnerability  (eds McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J. & White, K. S.)</font></br>

**Exposure** is the immediate effect

**Sensitivity** represents the longstanding innate properties of the population

**Adaptive capacity** are the things that can be done to change impacts

## Our approach

We aim to create a vulnerability index for the Brisbane City local government area (LGA) predicting the vulnerability of each level 2 statistical area (SA2).

<font size="4"> 
**Exposure**: flood extent intersection with properties, services, infrastructure

<font size="3"> 
  <ul>
  <li>rules for certain services like power, buffer of affected area</li>
  <li>various levels of exposure</li>
  <li>measure changed access to services with flooded roads</li>
  </ul>
</font>

**Sensitivity**: SA2 averages of demographic and socio-economic census data

**Adaptive capacity**: actions in place to increase resilience to flooding

<font size="3"> 
  <ul>
  <li>buy back programs</li>
  <li>moving towns, [Grantham](https://theconversation.com/moving-grantham-relocating-flood-prone-towns-is-nothing-new-4878)</li>
  <li>minimum build heights</li>
  </ul>
</font>
</font>

## R Libraries

```{r echo=TRUE}
# simple features package for vector spatial data
library(sf)
# for data manipulation
library(tidyverse)
# Australian maps
library(ozmaps)
```

Code is courtesy of Rich Cotrell

Great data from Queensland Spatial Catalogue - [QSpatial](https://qldspatial.information.qld.gov.au/catalogue/custom/index.page)

## Flood awareness plot

```{r echo=-1, results=FALSE, tidy=TRUE, out.width="70%"}
path <- "C:/Users/kimc7/OneDrive - Queensland University of Technology/flooding_vulnerability/data/"

flood_extents_shp <- st_read(paste0(path,
"spatial/Flood__Awareness__Extents/Flood___Awareness___Extents.shp"))

ggplot()+
  geom_sf(data = flood_extents_shp, fill="lightblue", colour = "grey90", size = 0.01)+
  theme_bw()
```

## Local Government Areas - {ozmaps}

```{r echo=TRUE, results=FALSE, cache=TRUE}
lgas <- ozmaps::ozmap_data(data = "abs_lga")

bris_lga <- lgas %>% filter(NAME == unique(lgas$NAME)[grep("Brisbane", unique(lgas$NAME))]) %>% st_transform(crs = st_crs(flood_extents_shp))
```

## Level 2 Statistical Area

From the Australian Bureau of Statistics:

```{r echo=TRUE, results=FALSE}
SA2_aus <- st_read(paste0(path, "spatial/sa2_2016_aust_shape/SA2_2016_AUST.shp"))

SA2_bris <- SA2_aus %>% 
  filter(STE_NAME16 == "Queensland" & 
           GCC_NAME16 == "Greater Brisbane" &
           SA4_NAME16 %in%  c("Brisbane - East","Brisbane - North", "Brisbane - South", "Brisbane - West", "Brisbane Inner City") & 
           (! SA2_NAME16 %in% c("Redland Islands"))) %>% 
  st_transform(crs = st_crs(flood_extents_shp))
```

## Plot LGAs and SA2s

```{r, echo=TRUE, eval=FALSE}
ggplot()+ geom_sf(data= bris_lga) + labs(title = "LGA") +
  theme(plot.title = element_text(size=30)) +
  theme_bw()
ggplot()+  geom_sf(data= SA2_bris) + labs(title = "SA2s") +
  theme(plot.title = element_text(size=30)) +
  theme_bw()
```

```{r, out.width="50%"}
ggplot()+ geom_sf(data= bris_lga) + labs(title = "LGA") +
  theme_bw()

ggplot()+  geom_sf(data= SA2_bris) + labs(title = "SA2s") +
  theme_bw()
```

## Emergency Services

```{r echo=TRUE}
ems <- st_read(paste0(path, "/spatial/QSC_QLD emergency service locations/Emergency_services_facilities.shp")) %>% 
  st_transform(crs = st_crs(flood_extents_shp))

ems_crop <- st_crop(x = ems, y = st_bbox(flood_extents_shp)) %>% filter(ATTRIBUTES %in% c("Queensland Ambulance Service", "Queensland Fire and Emergency Services", "Queensland Police Service"))

ems_intersect <- st_intersection(ems_crop, SA2_bris)
```

## Plot Emergency Services

```{r plot EMS, echo=TRUE, out.width="80%"}
ggplot()+
  geom_sf(data = SA2_bris, alpha=0, size =0.5) +
  geom_sf(data = ems_intersect, aes(colour = ATTRIBUTES), size= 1)+
  theme_bw()
```

## Petrol stations

```{r, echo=-4}
petrol <- st_read(paste0(path, "spatial/Petrol_Stations_Australia/PetrolStations.gdb")) %>% st_transform(crs = st_crs(flood_extents_shp))

petrol_crop <- st_crop(x = petrol, y = st_bbox(SA2_bris))

petrol_intersect <- st_intersection(x = petrol_crop, y = SA2_bris)
```

## Putting it all together | Flood extent with emergency services and petrol stations

```{r, out.width="110%"}
ggplot()+
   geom_sf(data = flood_extents_shp, size=0.01, fill="lightblue", alpha = 1)+
  geom_sf(data = SA2_bris, alpha=0, size =0.2, colour= "grey50")+
  geom_sf(data = ems_intersect, aes(colour = ATTRIBUTES), size= 1)+
  geom_sf(data = petrol_intersect, aes(fill = OPERATIONALSTATUS), size = 1, shape = 17)+
  theme_bw()+
  labs(colour = "EMS", fill="Petrol stations")
```

## Work in Progress

How to turn this in to an index?

- Combine all the variables of each component (Exposure etc) into a score 
- Add exposure, sensitivity, adaptive capacity and scale

## Future Work - Dream RShiny App!

Example: [SpatialEpiApp](https://community.rstudio.com/t/shiny-contest-submission-spatialepiapp-for-disease-surveillance/22699) for disease surveillance by Paula Moraga.

<img src="https://community.rstudio.com/uploads/default/original/2X/c/c13be2cb13791b8f44e7f8fd08038bd5960ba41c.gif" width = "800">

<br><font size="2"> 
 SpatialEpiApp is a Shiny web application that allows to visualize spatial and spatio-temporal disease data, estimate disease risk, and detect clusters. 
</font></br>
   
## Team

<center>
<img src="images/CKim.jpg" alt="Catherine Kim" width = 25%>
<img src="images/KBrown.jpg" alt="Kaitlyn Brown" width = 25%>
<img src="images/RCotrell_sq_2022-05-079.jpg" alt="Richard Cottrell" width = 25%>
<img src="images/JHopf.jpg" alt="Jess Hopf" width = 25%>
<img src="images/APrice.jpg" alt="Aiden price" width = 25%>
<img src="images/Connie-Susilawati.jpg" alt="Connie Susilawati" width = 25%>
</centre>

Thank you!

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